Greed Is Good: Guided Generation from a Greedy Perspective

Abstract

Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of diffusion models. In this work, we explore the guided generation from the perspective of optimizing the solution trajectory of a neural differential equation in a greedy manner. We present such a strategy as a unifying view on training-free guidance by showing that the greedy strategy is a first-order discretization of end-to-end optimization techniques. We show that a greedy guidance strategy makes *good* decisions and compare it to a guidance strategy using the *ideal* gradients found via the continuous adjoint equations. We then show how other popular training-free guidance strategies can be viewed in a unified manner from this perspective.